Create a Linear Regression
Here are the steps
to create a linear regression:
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Minimize the Decision Tree
visualization and
Tree Overview window.
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In this example, the
variable of interest is Age at Death, which
should be the first variable listed in the Measure section
of the Data pane.
Because you want this variable to be the
response variable, click, drag, and drop
Age
at Death from the
Data pane
onto the model pane. Notice that
Age at Death now
appears in the
Response field on the
Roles tab.
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Choose the effect variables or interaction terms that you want to include in the analysis.
One option is to make every available variable an effect variable and let SAS Visual
Statistics perform
variable selection. However, this is not always feasible from a
computational resources perspective. This example creates an
interaction term to use as an effect variable and includes a few other variables as effect variables.
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Because you suspect that systolic blood pressure and diastolic blood pressure interact
with each other, create an interaction term for these variables.
Follow these steps
to create an interaction term:
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In the
Data pane,
click
, and select
New Interaction Effect.
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In the
New Interaction Effect window, move
Diastolic and
Systolic from
the
Available columns area into the
Effect
elements area.
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The interaction term
Diastolic*Systolic appears
in the
Interaction Effects group of the
Data pane.
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Click, drag, and drop
Diastolic*Systolic onto the model pane. A model is created based on that
single effect because the
Auto-update model option is selected
in the right pane.
Tip
Each time a change is made
to the model, the Linear Regression automatically updates. If you
anticipate making many changes or if you are experiencing server performance
issues, deselect the
Auto-update model option.
When auto-updates are disabled, you must click
Update in
the right pane to update the model.
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Add more effects to
the model. Hold down the Ctrl key, and select
Blood Pressure
Status,
Cause of Death,
Leaf ID 1,
Sex,
Smoking
Status,
Cholesterol,
Height,
Smoking,
and
Weight. Drag and drop these variables
onto the model pane. The Linear Regression updates to include these
effects.
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In the right pane, select
the
Properties tab. In this model,
Informative missingness and
Use variable selection are
not selected. Disabling
Informative missingness means
that observations with missing values are not included in the analysis.
Disabling
Use variable selection means that
all variables are used in the model, regardless of how significant
they are to the model. For this model, keep the default properties
settings.
The
Fit Summary window indicates that
Cause of Death,
Leaf ID (1), and
Height are the three
most important effects in this model.
The Assessment window
indicates that the observed average and predicted average are approximately
equal for most bins.
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Last updated: January 8, 2019